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====== Eigenvalues and eigenvectors ======
In linear algebra, various linear transformations may be applied to a vector, often affecting the magnitude and direction of the vector. However, a given transformation, $T$, can be associated with an **eigenvector**, a vector whose direction is unchanged by the transformation. While the direction remains unchanged, the eigenvector of a transformation is scaled by a corresponding **eigenvalue**, $\lambda$.

The linear transformation $A$ may have many eigenvectors. An eigenvector $\vec{v}$ of $A$ should satisfy the following: 

$A\vec{v} = \lambda v$

==== Examples ====
Consider the matrix $\displaystyle A = \begin{bmatrix}2 & 1 \\ 1 & 2\end{bmatrix}$\\
Find the corresponding eigenvectors and eigenvalues for $A$.

==== See also ====
  * [[article:linear_algebra|Linear algebra]]
  * [[article:vector|Vector]]